We have one or two positions available for undergrad and graduate interns
Title: Studying the effect of tutor learning using SimStudent as a teachable agent
Project Description: The primary purpose of the SimStudent Teachable Agent project (www.SimStudent.org) is to study cognitive and social theories on the effect of tutor learning -- a well-known effect that students learn when they teach their peers. To study how, when, and why the effect of tutor learning happens, we have built an on-line game-like learning environment (called APLUS) where students learn how to solve algebra equations by teaching an interactive computer agent, i.e., SimStudent.
SimStudent and APLUS provide a rich study environment in which we can conduct a various controlled studies to explore the cognitive and social factors on the effect of tutor learning. For internship, there will also be opportunities to study topics in computer science such like improving SimStudent’s learning mechanism, or issues in human-computer interaction such like improving interactions between (human) students and SimStudent. For each individual summer intern projects, we will provide an independent research project based on the intern's interest and experience. There are a number of potential projects that would be suitable for a summer intern projects including (but not limited to) the ones listed below:
(1) Educational Data-mining Study – How do students learn by teaching? Using the study data mentioned above, a primary goal of the REU project would be to explore cognitive and social factors that mediate tutor learning. Knowledge and experience in advanced statistical analysis and/or data-mining techniques would be required.
(2) Usability Study – What makes the Learning-by-Teaching environment more user-friendly, hence facilitating better tutor learning? From a human-computer interaction point of view, improvement of the systems’ usability is an essential key for success in accomplishing our research agenda. In this line of research project, the REU student intern would apply various HCI methods to evaluate the system’s usability and explore key HCI factors to maximize tutor learning. Knowledge and experience in HCI methods would be required.
(3) Prior Knowledge Study – How does the “individual” differences of SimStudent (i.e., the tutee) affect the student’s (i.e., the tutor’s) learning? By manipulating the background knowledge of SimStudent, we can control the speed and accuracy of SimStudent’s learning. For example, SimStudent may start with a certain amount of knowledge for equation solving, or SimStudent might have immature or even irrelevant background knowledge that slows down learning rate and causes more errors. The goal of an REU project would then to study how such differences affect the tutor learning.
(4) Pedagogical Agent Study – How would the appearance of SimStudent and its functionality affect the tutor learning? What if SimStudent has emotion and expresses its affective status? Can SimStudent share its affects with the student and if so how would such a sympathetic pedagogical-agent influence the tutor learning? The goal of an REU project would be to study an affective interaction between SimStudent and human student and to understand how such emotional interaction would affect tutor learning.
(5) Emotional Pedagogical Agent Study – How could we improve the interaction between SimStudent and the students? So far, SimStudent only learns from the steps demonstrated. It would be more natural and (perhaps) pedagogically more appropriate if the student (i.e., the tutor) could give a hint with his/her own everyday language (e.g., “you can subtract the same number from both sides”). Such a natural language input could be used as a heuristic to navigate the search for induction. The goal of the REU project here is to study a rich tutoring dialogue by implementing and testing an augmented dialogue facility in the learning-by-teaching environment.
(6) Machine-Learning Study – Can we improve the SimStudent’s learning algorithm? So far, we used inductive logic programming that is basically implemented as a brute forth search. We also use FOIL (Quinlan, 1990), which learns Horn Clauses from relations provided in examples. There are pros and cons for the current implementation (mostly the issues for domain generality and knowledge representation). The REU on this project would study alternative technologies to enhance the generality and efficiency of SimStudent’s learning.
Contact: Noboru Matsuda <Noboru.Matsuda@cs.cmu.edu>
Human Computer Interaction Institute
Carnegie Mellon University
5000 Forbes Ave. Pittsburgh, PA 15213
Voice: 412-268-2357 Fax: 412-268-9433